Using Rasch Analysis to Generate Health State Values: Flushing

Previous methods used to develop a preference-based measure from an existing measure of health rely on the assumption that dimensions are independent. However, for condition-specific measures this may not always be the case. This project developed methodology that can be used to generate health state values for an existing measure when items are not independent.

The project developed health state values for flushing using the Flushing Symptoms Questionnaire (FSQ). The FSQ has items that are highly correlated (not independent). For example if a respondent has problems with redness of skin they are also likely to have problems with warmth, tingling and itching of skin.

The methodology had six stages:

Rasch analysis was used to develop a health state classification system from the FSQ and establish unidimensionality

Rasch item threshold maps were used to derive plausible health states for valuation

A valuation survey was conducted using time trade-off for a random general population sample of the UK

Mean utility values for the health states included in the valuation suvey were generated (using regression analysis to account for any differences explained by personal characteristics of the people valuing the health states)

Mean utility values were mapped onto the Rasch model logit scale, using the logit values obtained from the Rasch model that generated the original health states

The mapping function was used to generate health state values for all states